Analysis of Crop Yield Prediction using Machine Learning algorithms

Krishna, Vamsi; Reddy, Thejeswar; Harsha, S.; Ramar, K.; Hariharan, Shanmugasundaram; Bhanuprasad, A

doi:10.1109/cisct55310.2022.10046581

Cited by 10 publications

(5 citation statements)

References 11 publications

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“…The results showcase a remarkable 97% accuracy for KNN, outshining the Random Forest's 75% and Linear Regression's 54%, highlighting the promise of KNN in predictive agriculture and offering a data-driven beacon for enhancing agricultural productivity and informing farmers' decision-making processes [3]. (Krishna et al, 2022).…”

Section: Literature Reviewmentioning

confidence: 93%

“…By employing Python tools for data ltration and a Multilayer perceptron neural network for model development, the researchers initially reported a 45% accuracy using RMSprop optimizer, which was substantially improved to 90% by re ning the network architecture and shifting to the Adam optimizer. The model employs a 3-Layer Neural Network with the Recti ed Linear Activation Unit (ReLU) function, and leverages both backward and forward propagation techniques to establish a robust model for crop yield prediction [4]. (Kale & Patil, 2019).…”

Section: Literature Reviewmentioning

confidence: 99%

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Naïve Bayes and Random Forest for Crop Yield Prediction

Maazallahi,

Thota,

Kondaboina

et al. 2024

Preprint

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This study analyzes crop yield prediction in India from 1997 to 2020, focusing on various crops and key environmental factors. It aims to predict agricultural yields by utilizing advanced machine learning techniques like Linear Regression, Decision Tree, KNN, Naïve Bayes, K-Mean Clustering, and Random Forest. The models, particularly Naïve Bayes and Random Forest, demonstrate high effectiveness, as shown through data visualizations. The research concludes that integrating these analytical methods significantly enhances the accuracy and reliability of crop yield predictions, offering vital contributions to agricultural data science.

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Section: Literature Reviewmentioning

confidence: 93%

Section: Literature Reviewmentioning

confidence: 99%

Naïve Bayes and Random Forest for Crop Yield Prediction

Maazallahi,

Thota,

Kondaboina

et al. 2024

Preprint

View full text Add to dashboard Cite

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“…Vamsi Krishna et al [4] designed a method based on five features: season, area, temperature, rainfall, and crop name. They obtained an accuracy of 67% for the linear regression technique, 75% for the random forest algorithm, and 97% for the k-nearest neighbor approach.…”

Section: Literature Surveymentioning

confidence: 99%

Utilization of Machine Learning Algorithms for Precision Agriculture: Enhancing Crop Selection

Kakade,

Kulkarni,

Dhawale

et al. 2023

Green Intell. Syst. Appl.

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Agriculture stands as a crucial economic driver, playing a pivotal role in fostering economic progress. Understanding the dynamics of the agricultural system is imperative for ensuring food security. Even as technological strides like vertical farming emerge, conventional farming practices and beliefs continue to hold sway. This study delves into fundamental aspects such as soil composition, pH levels, humidity, and rainfall, employing a range of machine learning models including kernel naive Bayes, Gaussian naive Bayes, linear support vector machine (SVM), quadratic discriminant analysis, and quadratic SVM. The primary objective is to provide insightful crop recommendations to farmers. Accurate crop forecasting is paramount for optimizing agricultural methodologies and maintaining a consistent food supply. By leveraging historical weather trends, soil quality, and crop production data, machine learning algorithms proficiently anticipate crop yields. The outcomes of this investigation have the potential to refine crop management practices and reinforce food security measures.

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“…The prediction was made by the system using factors including the state, crop, temperature, and rainfall. After comparing these algorithms, the K-nearest neighbour has shown the highest accuracy [25]. Using the SVR, DT, RF, and GB regression approaches, Kumar et al [26] estimated the wheat production yield.…”

Section: Introductionmentioning

confidence: 99%

Machine Learning-based Extrapolation of Crop Cultivation Cost

Bari,

Ragha

2024

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It is important to comprehend the relation between operational expenses such as labour, seed, irrigation, insecticides, fertilizers and manure costs necessary for the cultivation of crops. A precise cost for the cultivation of crops can offer vital information for agricultural decision-making. The main goal of the study is to compare machine learning (ML) techniques to measure relationships among operational cost characteristics for predicting crop cultivation costs before the start of the growing season using the dataset made available by the Ministry of Agriculture and Farmer Welfare of the Government of India. This paper describes various ML regression techniques, compares various learning algorithms as well as determines the most efficient regression algorithms based on the data set, the number of samples and attributes. The data set used for predicting the cost with 1680 instances includes varying costs for 14 different crops for 12 years (2010-2011 to 2021-2022). Ten different ML algorithms are considered and the crop cultivation cost is predicted. The evaluation results show that Random Forest (RF), Decision Tree (DT), Extended gradient boosting (XR) and K-Neighbours (KN) regression provide better performance in terms of coefficient of determination (R2), root mean square error (RMSE) and mean absolute error (MAE) rate while training and testing time. This study also compares different ML techniques and showed significant differences using the statistical analysis of variance (ANOVA) test. The optimal hyperparameters for the ML models are found using the gridsearchCV and randomizedsearchCV functions, which improves the model's capacity for generalisation.

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Analysis of Crop Yield Prediction using Machine Learning algorithms

Cited by 10 publications

References 11 publications

Naïve Bayes and Random Forest for Crop Yield Prediction

Naïve Bayes and Random Forest for Crop Yield Prediction

Utilization of Machine Learning Algorithms for Precision Agriculture: Enhancing Crop Selection

Machine Learning-based Extrapolation of Crop Cultivation Cost

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